First, import and tidy data:

UHF_zipcode = 
  read_csv("./data/appb.csv") %>% 
  slice(-43) %>% 
  select(-Borough) %>% 
  rename("UHF" = "UHF Neighborhood") %>% 
  janitor::clean_names()
raw_hiv = 
  GET("https://data.cityofnewyork.us/api/views/fju2-rdad/rows.csv") %>% 
  content("parsed") %>% 
  janitor::clean_names()

combine_hiv = 
  right_join(UHF_zipcode, raw_hiv, by = "uhf") %>%
  janitor::clean_names() %>% 
  separate(zip_code, into = c("zipcode1", "zipcode2", "zipcode3", 
                              "zipcode4", "zipcode5", "zipcode6", "zipcode7", "zipcode8",
                              "zipcode9"), sep = ", ") %>% 
  gather(key = zip_code, value = zipcode_value, zipcode1:zipcode9) %>% 
  filter(!is.na(zipcode_value)) %>% 
  rename("zipcode" = "zipcode_value") %>% 
  select(zipcode, everything(), -zip_code)
r = GET('http://data.beta.nyc//dataset/3bf5fb73-edb5-4b05-bb29-7c95f4a727fc/resource/6df127b1-6d04-4bb7-b983-07402a2c3f90/download/f4129d9aa6dd4281bc98d0f701629b76nyczipcodetabulationareas.geojson')
nyc_zipcode = readOGR(content(r,'text'), 'OGRGeoJSON', verbose = F)

zipcode_lat_lng = nyc_zipcode@data %>% 
  select(zipcode = postalCode, longitude, latitude) %>% 
  mutate(zipcode = as.character(zipcode))
  
combine_hiv1 = 
  full_join(zipcode_lat_lng, combine_hiv, by = "zipcode") %>% 
  mutate(longitude = as.numeric(longitude), latitude = as.numeric(latitude)) %>% 
  group_by(uhf) %>% 
  summarise(lng = mean(longitude),
            lat = mean(latitude)) %>% 
  filter(!(uhf == "Pelhem - Throgs Neck"))
pums_raw = read_csv("./data/selected_pums.csv")

temp = tempfile(fileext = ".xls")

dataURL = "http://faculty.baruch.cuny.edu/geoportal/resources/nyc_geog/nyc_zcta10_to_puma10.xls"

download.file(dataURL, destfile = temp, mode = 'wb')

zcta_to_puma = readxl::read_xls(temp, sheet = 2) %>% 
  select(zcta = zcta10, puma = puma10) %>% 
  mutate(puma = as.numeric(puma))

dataURL = "http://faculty.baruch.cuny.edu/geoportal/resources/nyc_geog/zip_to_zcta10_nyc_revised.xls"

download.file(dataURL, destfile = temp, mode = 'wb')
zip_to_zcta = readxl::read_xls(temp, sheet = 2) %>% 
  select(zipcode, zcta = zcta5) 
pums_data = 
  pums_raw %>% 
  select(puma = PUMA10, income = PINCP, year = ADJINC) %>% 
  filter(puma != -9)  # remove data from 2011 due to lack of area information

pums_data$year = recode(pums_data$year, 
                        "1042852" = "2012",
                        "1025215" = "2013",  
                        "1009585" = "2014", 
                        "1001264" = "2015")   

pums_data = 
  pums_data %>% 
  group_by(year, puma) %>% 
  summarise(mid_income = median(income, na.rm = TRUE)) %>% 
  ungroup()         # calculate yearly median income for each area
puma_to_zipcode = right_join(zip_to_zcta, zcta_to_puma, by = "zcta") %>%   # generaate a puma to zipcode file
  select(puma, zipcode)

income_zipcode = right_join(pums_data, puma_to_zipcode, by = "puma") %>%  # matching zipcode with median income data
  select(year, zipcode, mid_income) %>% 
  mutate(year = as.numeric(year))

combine_hiv_income = 
  left_join(combine_hiv, income_zipcode, by = c("year", "zipcode"))

hiv_data = raw_hiv

gender neighborhood VS hiv

neb_plot = hiv_data %>% 
  group_by(uhf, gender) %>% 
  filter(year != "ALL", borough != "All", uhf != "All", gender != "All") %>% 
  filter(age != "All") %>%
  summarise(sum_hiv = sum(hiv_diagnoses)) %>% 
  ggplot(aes(x = reorder(uhf, sum_hiv), y = sum_hiv, color = gender)) + 
  coord_flip() +
  geom_point() +
  labs(
        title = "Gender and Neighborhood Influence on HIV Incidence",
        x = "Neighborhood",
        y = "HIV diagnoses",
        caption = "Data from the ..."
      )

ggplotly(neb_plot)

Interpretation: The number of HIV diagnoses is apperently higher among male subgroups than female in all neighborhoods. Beford Stuyvesant - Crown Heights have the highest total HIV diagnoses and highest female HIV diagnoses cases. Chelsea - Clinton ranks first in male HIV diagnoses. Bayside - Little Neck has lowest number of HIV diagnoses for both male and female.

Gender, age vs hiv

age_plot = hiv_data %>% 
  filter(race == "All" & borough == "All" & age != "All") %>% 
  group_by(gender, age) %>% 
  summarise(sum_hiv = sum(hiv_diagnoses)) %>% 
  ggplot(aes(y = sum_hiv, x = age, fill = gender)) + 
  geom_bar(stat = "identity", alpha = 0.8, position = position_dodge()) +
  scale_fill_brewer(palette = "Dark2") +
  labs(
        title = "Gender and Age Influence on HIV Incidence",
        x = "Age range",
        y = "HIV diagnoses",
        caption = "Data from the ..."
      ) 

ggplotly(age_plot)

Interpretation: As is shown in the barchart above, in every age range, HIV incidence rate in male is significantly higher than that in female population. The potential explaination could be the gender differences with respect to HIV/AIDS depend on patterns of disease transmission. Most infections occurred in adults aged 20 to 29 years, and the incidence porpotion declines as the increase of age.

Gender race vs hiv

race_plot = hiv_data %>% 
  filter(age == "All" & borough == "All" & race != "All") %>% 
  group_by(gender, race) %>% 
  summarise(sum_hiv = sum(hiv_diagnoses)) %>% 
  ggplot(aes(y = sum_hiv, x = reorder(race, sum_hiv), fill = gender)) + 
  geom_bar(stat = "identity", alpha = 0.8, position = position_dodge()) +
  scale_fill_manual(values = c("#E69F00", "#56B4E9")) +
  labs(
        title = "Race and Gender Influence on HIV Incidence",
        x = "Race",
        y = "HIV diagnoses",
        caption = "Data from the ..."
      ) 

ggplotly(race_plot)

Interpretation: By race/ethnicity, black men/women have the highest rates of new HIV infections among all men/women. Whereas the incidence rate among Asian/Pacific Islander is the lowest given the study population in NYC. This is because some race population groups have higher rates of HIV in their communities, thus raising the risk of new infections with each sexual or drug use encounter. Plus, social, economic, and demographic factors of various race group—such as stigma, discrimination, income, education, and geographic region—could also affect their risk for HIV.

hiv diagnoses in borough with most hiv over years

hiv_data %>%
  filter(borough != "All", uhf == "All", gender == "All", age == "All", race == "All") %>% 
  group_by(borough) %>% 
  summarize(sum_hiv = sum(hiv_diagnoses)) %>% 
  arrange(desc(sum_hiv))
## # A tibble: 5 x 2
##   borough       sum_hiv
##   <chr>           <int>
## 1 Brooklyn         3815
## 2 Manhattan        3536
## 3 Bronx            2736
## 4 Queens           2327
## 5 Staten Island     217
year_plot = hiv_data %>% 
  mutate(year = as.integer(year)) %>% 
  filter(borough == "Brooklyn" & gender == "Male" & age == "20 - 29") %>% 
  group_by(year, uhf) %>% 
  summarize(sum_hiv = sum(hiv_diagnoses)) %>% 
  ggplot(aes(x = year, y = sum_hiv, color = uhf)) +
  geom_line() +
  labs(
        title = "Race and Gender Influence on HIV Incidence",
        x = "Year",
        y = "Sum of HIV diagnoses",
        caption = "Data from the ..."
      ) 
  
ggplotly(year_plot)
overall_year = hiv_data %>%
  filter(borough == "All", uhf == "All", age == "All", race == "All") %>% 
  group_by(year, gender) %>% 
  summarize(sum_hiv = sum(hiv_diagnoses)) %>% 
  ggplot(aes(x = year, y = sum_hiv, group = gender, color = gender)) +
  geom_line() +
  labs(
        title = "Yearly change trend on HIV Incidence",
        x = "Year",
        y = "Sum of HIV diagnoses",
        caption = "Data from the ..."
      ) 
ggplotly(overall_year)

Interpretation: The overall trend of total HIV incidence among study population in NYC on the decline from 2011 to 2015. The decrease in male is much significant than in female, and not very obvious in transgender group for theie low base account. The downward trend in HIV incidence rate reflects the improvment of public health practice affect the HIV incidence rate through repeated exposure to counseling (such as the promotion of condom use or safe sex or other prevention messages) and the advances in HIV treatments.